Kozy, Mark Alexander2019-06-132019-06-132019-06-12vt_gsexam:20201http://hdl.handle.net/10919/89948In this paper we address radar-communication coexistence by modelling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system to improve its performance. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations. In this thesis we also address the application of the MDP into a software defined radio (SDR) USRP X310 by utilizing the software LabVIEW to communicate with and control the SDR.ETDIn CopyrightCognitive radarMachine learningreinforcement learningtracking radarsoftware defined radioThesis